Merge pull request #1559 from bmaltais/dev2

v22.0.0

.release

@@ -1 +1 @@
-v21.8.10
+v22.0.0

.vscode/settings.json

@@ -1,4 +1,5 @@
 {
     "python.linting.enabled": true,
-    "python.formatting.provider": "yapf"
+    "python.formatting.provider": "yapf",
+    "DockerRun.DisableDockerrc": true
 }

README-ja.md

@@ -1,3 +1,7 @@
+SDXLがサポートされました。sdxlブランチはmainブランチにマージされました。リポジトリを更新したときにはUpgradeの手順を実行してください。また accelerate のバージョンが上がっていますので、accelerate config を再度実行してください。
+
+SDXL学習については[こちら](./README.md#sdxl-training)をご覧ください（英語です）。
+
 ## リポジトリについて
 Stable Diffusionの学習、画像生成、その他のスクリプトを入れたリポジトリです。
 
@@ -9,13 +13,12 @@ GUIやPowerShellスクリプトなど、より使いやすくする機能が[bma
 
 * DreamBooth、U-NetおよびText Encoderの学習をサポート
 * fine-tuning、同上
+* LoRAの学習をサポート
 * 画像生成
 * モデル変換（Stable Diffision ckpt/safetensorsとDiffusersの相互変換）
 
 ## 使用法について
 
-当リポジトリ内およびnote.comに記事がありますのでそちらをご覧ください（将来的にはすべてこちらへ移すかもしれません）。
-
 * [学習について、共通編](./docs/train_README-ja.md) : データ整備やオプションなど
     * [データセット設定](./docs/config_README-ja.md)
 * [DreamBoothの学習について](./docs/train_db_README-ja.md)
@@ -41,11 +44,13 @@ PowerShellを使う場合、venvを使えるようにするためには以下の
 
 ## Windows環境でのインストール
 
-以下の例ではPyTorchは1.12.1／CUDA 11.6版をインストールします。CUDA 11.3版やPyTorch 1.13を使う場合は適宜書き換えください。
+スクリプトはPyTorch 2.0.1でテストしています。PyTorch 1.12.1でも動作すると思われます。
+
+以下の例ではPyTorchは2.0.1／CUDA 11.8版をインストールします。CUDA 11.6版やPyTorch 1.12.1を使う場合は適宜書き換えください。
 
 （なお、python -m venv～の行で「python」とだけ表示された場合、py -m venv～のようにpythonをpyに変更してください。）
 
-通常の（管理者ではない）PowerShellを開き以下を順に実行します。
+PowerShellを使う場合、通常の（管理者ではない）PowerShellを開き以下を順に実行します。
 
 ```powershell
 git clone https://github.com/kohya-ss/sd-scripts.git
@@ -54,43 +59,14 @@ cd sd-scripts
 python -m venv venv
 .\venv\Scripts\activate
 
-pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
+pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118
 pip install --upgrade -r requirements.txt
-pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl
-
-cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
-cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
-cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py
+pip install xformers==0.0.20
 
 accelerate config
 ```
 
-<!-- 
-pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
-pip install --use-pep517 --upgrade -r requirements.txt
-pip install -U -I --no-deps xformers==0.0.16
--->
-
-コマンドプロンプトでは以下になります。
-
-
-```bat
-git clone https://github.com/kohya-ss/sd-scripts.git
-cd sd-scripts
-
-python -m venv venv
-.\venv\Scripts\activate
-
-pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
-pip install --upgrade -r requirements.txt
-pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl
-
-copy /y .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
-copy /y .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
-copy /y .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py
-
-accelerate config
-```
+コマンドプロンプトでも同一です。
 
 （注:``python -m venv venv`` のほうが ``python -m venv --system-site-packages venv`` より安全そうなため書き換えました。globalなpythonにパッケージがインストールしてあると、後者だといろいろと問題が起きます。）
 
@@ -111,29 +87,40 @@ accelerate configの質問には以下のように答えてください。（bf1
 ※場合によって ``ValueError: fp16 mixed precision requires a GPU`` というエラーが出ることがあるようです。この場合、6番目の質問（
 ``What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:``）に「0」と答えてください。（id `0`のGPUが使われます。）
 
-### PyTorchとxformersのバージョンについて
+### オプション：`bitsandbytes`（8bit optimizer）を使う
 
-他のバージョンでは学習がうまくいかない場合があるようです。特に他の理由がなければ指定のバージョンをお使いください。
+`bitsandbytes`はオプションになりました。Linuxでは通常通りpipでインストールできます（0.41.1または以降のバージョンを推奨）。
 
-### オプション：Lion8bitを使う
+Windowsでは0.35.0または0.41.1を推奨します。
 
-Lion8bitを使う場合には`bitsandbytes`を0.38.0以降にアップグレードする必要があります。`bitsandbytes`をアンインストールし、Windows環境では例えば[こちら](https://github.com/jllllll/bitsandbytes-windows-webui)などからWindows版のwhlファイルをインストールしてください。たとえば以下のような手順になります。
+- `bitsandbytes` 0.35.0: 安定しているとみられるバージョンです。AdamW8bitは使用できますが、他のいくつかの8bit optimizer、学習時の`full_bf16`オプションは使用できません。
+- `bitsandbytes` 0.41.1: Lion8bit、PagedAdamW8bit、PagedLion8bitをサポートします。`full_bf16`が使用できます。
 
-```powershell
-pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl
-```
+注：`bitsandbytes` 0.35.0から0.41.0までのバージョンには問題があるようです。 https://github.com/TimDettmers/bitsandbytes/issues/659
 
-アップグレード時には`pip install .`でこのリポジトリを更新し、必要に応じて他のパッケージもアップグレードしてください。
+以下の手順に従い、`bitsandbytes`をインストールしてください。
 
-### オプション：PagedAdamW8bitとPagedLion8bitを使う
+### 0.35.0を使う場合
 
-PagedAdamW8bitとPagedLion8bitを使う場合には`bitsandbytes`を0.39.0以降にアップグレードする必要があります。`bitsandbytes`をアンインストールし、Windows環境では例えば[こちら](https://github.com/jllllll/bitsandbytes-windows-webui)などからWindows版のwhlファイルをインストールしてください。たとえば以下のような手順になります。
+PowerShellの例です。コマンドプロンプトではcpの代わりにcopyを使ってください。
 
 ```powershell
-pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
+cd sd-scripts
+.\venv\Scripts\activate
+pip install bitsandbytes==0.35.0
+
+cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
+cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
+cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py
 ```
 
-アップグレード時には`pip install .`でこのリポジトリを更新し、必要に応じて他のパッケージもアップグレードしてください。
+### 0.41.1を使う場合
+
+jllllll氏の配布されている[こちら](https://github.com/jllllll/bitsandbytes-windows-webui) または他の場所から、Windows用のwhlファイルをインストールしてください。
+
+```powershell
+python -m pip install bitsandbytes==0.41.1 --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui
+```
 
 ## アップグレード
 

README.md

@@ -42,6 +42,12 @@ The GUI allows you to set the training parameters and generate and run the requi
     - [Page File Limit](#page-file-limit)
     - [No module called tkinter](#no-module-called-tkinter)
     - [FileNotFoundError](#filenotfounderror)
+  - [SDXL training](#sdxl-training)
+    - [Training scripts for SDXL](#training-scripts-for-sdxl)
+    - [Utility scripts for SDXL](#utility-scripts-for-sdxl)
+    - [Tips for SDXL training](#tips-for-sdxl-training-1)
+    - [Format of Textual Inversion embeddings for SDXL](#format-of-textual-inversion-embeddings-for-sdxl)
+    - [ControlNet-LLLite](#controlnet-lllite)
   - [Change History](#change-history)
 
 
@@ -531,8 +537,118 @@ If you come across a `FileNotFoundError`, it is likely due to an installation is
 
    After uninstalling the local packages, redo the installation steps within the `kohya_ss` virtual environment.
 
+
+## SDXL training
+
+The documentation in this section will be moved to a separate document later.
+
+### Training scripts for SDXL
+
+- `sdxl_train.py` is a script for SDXL fine-tuning. The usage is almost the same as `fine_tune.py`, but it also supports DreamBooth dataset.
+  - `--full_bf16` option is added. Thanks to KohakuBlueleaf!
+    - This option enables the full bfloat16 training (includes gradients). This option is useful to reduce the GPU memory usage. 
+    - The full bfloat16 training might be unstable. Please use it at your own risk.
+  - The different learning rates for each U-Net block are now supported in sdxl_train.py. Specify with `--block_lr` option. Specify 23 values separated by commas like `--block_lr 1e-3,1e-3 ... 1e-3`.
+    - 23 values correspond to `0: time/label embed, 1-9: input blocks 0-8, 10-12: mid blocks 0-2, 13-21: output blocks 0-8, 22: out`.
+- `prepare_buckets_latents.py` now supports SDXL fine-tuning.
+
+- `sdxl_train_network.py` is a script for LoRA training for SDXL. The usage is almost the same as `train_network.py`.
+
+- Both scripts has following additional options:
+  - `--cache_text_encoder_outputs` and `--cache_text_encoder_outputs_to_disk`: Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions.
+  - `--no_half_vae`: Disable the half-precision (mixed-precision) VAE. VAE for SDXL seems to produce NaNs in some cases. This option is useful to avoid the NaNs.
+
+- `--weighted_captions` option is not supported yet for both scripts.
+
+- `sdxl_train_textual_inversion.py` is a script for Textual Inversion training for SDXL. The usage is almost the same as `train_textual_inversion.py`.
+  - `--cache_text_encoder_outputs` is not supported.
+  - There are two options for captions:
+    1. Training with captions. All captions must include the token string. The token string is replaced with multiple tokens.
+    2. Use `--use_object_template` or `--use_style_template` option. The captions are generated from the template. The existing captions are ignored.
+  - See below for the format of the embeddings.
+
+- `--min_timestep` and `--max_timestep` options are added to each training script. These options can be used to train U-Net with different timesteps. The default values are 0 and 1000.
+
+### Utility scripts for SDXL
+
+- `tools/cache_latents.py` is added. This script can be used to cache the latents to disk in advance. 
+  - The options are almost the same as `sdxl_train.py'. See the help message for the usage.
+  - Please launch the script as follows:
+    `accelerate launch  --num_cpu_threads_per_process 1 tools/cache_latents.py ...`
+  - This script should work with multi-GPU, but it is not tested in my environment.
+
+- `tools/cache_text_encoder_outputs.py` is added. This script can be used to cache the text encoder outputs to disk in advance. 
+  - The options are almost the same as `cache_latents.py` and `sdxl_train.py`. See the help message for the usage.
+
+- `sdxl_gen_img.py` is added. This script can be used to generate images with SDXL, including LoRA, Textual Inversion and ControlNet-LLLite. See the help message for the usage.
+
+### Tips for SDXL training
+
+- The default resolution of SDXL is 1024x1024.
+- The fine-tuning can be done with 24GB GPU memory with the batch size of 1. For 24GB GPU, the following options are recommended __for the fine-tuning with 24GB GPU memory__:
+  - Train U-Net only.
+  - Use gradient checkpointing.
+  - Use `--cache_text_encoder_outputs` option and caching latents.
+  - Use Adafactor optimizer. RMSprop 8bit or Adagrad 8bit may work. AdamW 8bit doesn't seem to work.
+- The LoRA training can be done with 8GB GPU memory (10GB recommended). For reducing the GPU memory usage, the following options are recommended:
+  - Train U-Net only.
+  - Use gradient checkpointing.
+  - Use `--cache_text_encoder_outputs` option and caching latents.
+  - Use one of 8bit optimizers or Adafactor optimizer.
+  - Use lower dim (4 to 8 for 8GB GPU).
+- `--network_train_unet_only` option is highly recommended for SDXL LoRA. Because SDXL has two text encoders, the result of the training will be unexpected.
+- PyTorch 2 seems to use slightly less GPU memory than PyTorch 1.
+- `--bucket_reso_steps` can be set to 32 instead of the default value 64. Smaller values than 32 will not work for SDXL training.
+
+Example of the optimizer settings for Adafactor with the fixed learning rate:
+```toml
+optimizer_type = "adafactor"
+optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False" ]
+lr_scheduler = "constant_with_warmup"
+lr_warmup_steps = 100
+learning_rate = 4e-7 # SDXL original learning rate
+```
+
+### Format of Textual Inversion embeddings for SDXL
+
+```python
+from safetensors.torch import save_file
+
+state_dict = {"clip_g": embs_for_text_encoder_1280, "clip_l": embs_for_text_encoder_768}
+save_file(state_dict, file)
+```
+
+### ControlNet-LLLite
+
+ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See [documentation](./docs/train_lllite_README.md) for details.
+
+
 ## Change History
 
+* 2023/10/01 (v22.0.0)
+  - Merging main branch of sd-scripts:
+    - [SAI Model Spec](https://github.com/Stability-AI/ModelSpec) metadata is now supported partially. `hash_sha256` is not supported yet.
+      - The main items are set automatically. 
+      - You can set title, author, description, license and tags with `--metadata_xxx` options in each training script.
+      - Merging scripts also support minimum SAI Model Spec metadata. See the help message for the usage.
+      - Metadata editor will be available soon.
+
+    - `--v_pred_like_loss ratio` option is added. This option adds the loss like v-prediction loss in SDXL training. `0.1` means that the loss is added 10% of the v-prediction loss. The default value is None (disabled).
+      - In v-prediction, the loss is higher in the early timesteps (near the noise). This option can be used to increase the loss in the early timesteps.
+
+    - Arbitrary options can be used for Diffusers' schedulers. For example `--lr_scheduler_args "lr_end=1e-8"`.
+
+    - LoRA-FA is added experimentally. Specify `--network_module networks.lora_fa` option instead of `--network_module networks.lora`. The trained model can be used as a normal LoRA model.
+    - JPEG XL is supported. [#786](https://github.com/kohya-ss/sd-scripts/pull/786) 
+    - Input perturbation noise is added. See [#798](https://github.com/kohya-ss/sd-scripts/pull/798) for details.
+    - Dataset subset now has `caption_prefix` and `caption_suffix` options. The strings are added to the beginning and the end of the captions before shuffling. You can specify the options in `.toml`.
+    - Intel ARC support with IPEX is added. [#825](https://github.com/kohya-ss/sd-scripts/pull/825)
+    - Other bug fixes and improvements.
+  - New SDXL presets
+  - Update wandb module version
+  - Add support for Chinese zh-CN localisation. You can use it with `.\gui.bat --language=zh-CN`
+  - Add presets support to `Finetuning`. You can add your own finetuning user presets under the `/presets/finetune/user_presets` folder.
+
 * 2023/09/23 (v21.8.10)
   - Minor point upgrade. Mostly adding a new preset.
 
@@ -544,4 +660,4 @@ If you come across a `FileNotFoundError`, it is likely due to an installation is
     * Input perturbation noise is added. See #798 for details.
     * Dataset subset now has caption_prefix and caption_suffix options. The strings are added to the beginning and the end of the captions before shuffling. You can specify the options in .toml.
     * Other minor changes.
-  - Added support for Chinese locallisation
+  - Added support for Chinese locallisation

XTI_hijack.py

@@ -1,4 +1,11 @@
 import torch
+try:
+    import intel_extension_for_pytorch as ipex
+    if torch.xpu.is_available():
+        from library.ipex import ipex_init
+        ipex_init()
+except Exception:
+    pass
 from typing import Union, List, Optional, Dict, Any, Tuple
 from diffusers.models.unet_2d_condition import UNet2DConditionOutput